SAI

Using explainable AI to identify regional climate signals in response to stratospheric aerosol injection

Under construction... [Python 3.7]

Project

Check out AI Methods for Solar Radiation Management Research (DARPA‐PA‐21‐04‐02) - https://github.com/eabarnes1010/actm-sai-csu

Contact

Zachary Labe - Research Website - @ZLabe

Description

• Scripts/: Main Python scripts/functions used in data analysis and plotting
• requirements.txt: List of environments and modules associated with the most recent version of this project. A Python Anaconda3 Distribution was used for our analysis. Tools including NCL, CDO, and NCO were also used for initial data manipulation.

Data

• Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE) : [DATA]

  • Richter, J., Visioni, D., MacMartin, D., Bailey, D. A., Lee, W., Woodhouse, S., ... & Lamarque, J. F. (2021, December). Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosols (ARISE-SAI) simulations. In AGU Fall Meeting 2021. AGU. [ABSTRACT]
  • Richter, J. H., Visioni, D., MacMartin, D. G., Bailey, D. A., Rosenbloom, N., Dobbins, B., ... & Lamarque, J. F. (2022). Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations. Geoscientific Model Development, 15(22), 8221-8243., doi:10.5194/gmd-15-8221-2022 [PUBLICATION]

• Whole Atmosphere Community Climate Model Version 6 (WACCM6) : [DATA]

  • Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., ... & Strand, W. G. (2020). The community earth system model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001916., doi:10.1029/2019MS001916 [PUBLICATION]
  • Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K., Marsh, D. R., ... & Randel, W. J. (2019). The whole atmosphere community climate model version 6 (WACCM6). Journal of Geophysical Research: Atmospheres, 124(23), 12380-12403., doi:10.1029/2019JD030943 [PUBLICATION]

Publications

• [1] Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023), Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a [HTML][SUMMARY][BibTeX]

Conferences

• [1] Labe, Z.M., E.A. Barnes, and J.W. Hurrell. Detecting the regional emergence of climate signals with machine learning in a set of stratospheric aerosol injection simulations, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022) [ABSTRACT][POSTER]